Exploratory analysis and QAQC

Defining functions and packages

# 1. Source functions ----

source("functions/graphical_par.R")
source("functions/theme_graph.R")
source("functions/pred_vbgf.R")

# 2. Required packages ----

library(tidyverse)
library(kableExtra)
library(formattable)
library(readxl)
library(plotly)
library(rfishbase)

# 3. Set theme_graph() as the default ggplot theme ----

theme_set(theme_graph())

# 4. Load data ----

data_complete <- read.csv("./../data/02_back-calculated-size-at-age_morat-et-al.csv")

1. Data exploration

1.1 Tables

1.1.1 Data with back-calc.

data_complete %>% 
  filter(!is.na(Li_sp_m)) %>% 
  summarize_at(vars("ID", "Species", "Family"), n_distinct, na.rm = TRUE) %>% 
  bind_rows(data_complete %>%
              filter(!is.na(Li_sploc_m)) %>% 
              summarize_at(vars("ID", "Species", "Family"), n_distinct, na.rm = TRUE), .) %>% 
  bind_rows(data_complete %>%
              summarize_at(vars("ID", "Species", "Family"), n_distinct, na.rm = TRUE), .) %>% 
  mutate(Type = c("All data", "Back-calculated sp. loc.", "Back-calculated sp."), .before = 1) %>% 
  kable(., 
        col.names = c("", "Individual", "Species", "Family"), 
        caption = "Table 1. Comparison of numbers of individuals, species and family for the overall dataset and for back-calculated data (by species, and by species and location).") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Table 1. Comparison of numbers of individuals, species and family for the overall dataset and for back-calculated data (by species, and by species and location).

	Individual	Species	Family
All data	855	51	16
Back-calculated sp. loc.	669	44	16
Back-calculated sp.	710	45	16

1.1.2 Individual by species

data_complete %>%
  filter(!is.na(Li_sp_m)) %>% 
  group_by(Species) %>% 
  summarise(n = length(unique(ID)),
        min_lencap = round(min(Lcpt, na.rm = TRUE), 0),
        max_lencap = round(max(Lcpt, na.rm = TRUE), 0),
        max_age = max(Agei, na.rm = TRUE)) %>% 
  kable(., 
        col.names = c("Species", "n", "Min length (TL, mm)", "Max length (TL, mm)", "Age max (years)"),
        caption = "Table 2. Number of individuals, minimum and maximum total length (mm) and maximum age, by species. Only individuals with back-calculated data by species are shown.") %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  column_spec(1, italic = T)

Table 2. Number of individuals, minimum and maximum total length (mm) and maximum age, by species. Only individuals with back-calculated data by species are shown.

Species	n	Min length (TL, mm)	Max length (TL, mm)	Age max (years)
Abudefduf sexfasciatus	13	61	172	13
Acanthurus achilles	16	138	246	27
Acanthurus lineatus	8	123	362	23
Acanthurus nigricans	8	150	210	9
Acanthurus pyroferus	6	195	230	19
Acanthurus triostegus	18	147	194	12
Balistapus undulatus	17	70	283	18
Caranx melampygus	7	368	712	15
Centropyge bispinosa	7	36	80	11
Centropyge flavissima	26	48	150	27
Cephalopholis argus	41	120	450	21
Cephalopholis urodeta	8	150	214	17
Chaetodon citrinellus	7	64	101	6
Chaetodon ornatissimus	10	122	175	10
Chlorurus spilurus	31	183	344	16
Chromis iomelas	15	41	61	5
Chromis viridis	6	115	148	9
Ctenochaetus marginatus	15	99	270	13
Ctenochaetus striatus	26	153	218	17
Dascyllus aruanus	20	51	71	7
Dascyllus flavicaudus	8	89	104	13
Epibulus insidiator	17	157	350	16
Epinephelus fasciatus	10	123	256	13
Epinephelus hexagonatus	14	160	268	14
Epinephelus merra	46	108	261	17
Epinephelus polyphekadion	14	252	514	20
Gnathodentex aureolineatus	8	197	255	17
Gymnosarda unicolor	4	499	985	7
Halichoeres trimaculatus	7	123	186	5
Lutjanus fulvus	12	195	289	21
Lutjanus kasmira	10	115	326	30
Mulloidichthys flavolineatus	14	257	311	6
Myripristis berndti	30	147	279	24
Naso lituratus	17	228	432	10
Odonus niger	17	176	350	16
Ostorhinchus angustatus	15	46	83	6
Ostorhinchus apogonoides	27	58	96	7
Parupeneus barberinus	6	228	496	6
Plectropomus laevis	30	399	957	22
Pristiapogon taeniopterus	28	53	104	8
Sargocentron microstoma	22	62	204	13
Scarus psittacus	7	195	311	6
Siganus argenteus	11	236	388	13
Stegastes nigricans	13	55	138	13
Zebrasoma scopas	18	110	166	24

1.1.3 Individuals by location

data_complete %>%
  filter(!is.na(Li_sp_m)) %>% 
  group_by(Species, Location) %>% 
  summarise(n = length(unique(ID))) %>% 
  pivot_wider(names_from = Location, values_from = n) %>% 
  mutate(Gambiers = cell_spec(Gambiers, "html", color = ifelse(is.na(Gambiers), "white", "#446CB3")),
         Hao = cell_spec(Hao, "html", color = ifelse(is.na(Hao), "white", "#446CB3")),
         Marquesas = cell_spec(Marquesas, "html", color = ifelse(is.na(Marquesas), "white", "#446CB3")),
         Moorea = cell_spec(Moorea, "html", color = ifelse(is.na(Moorea), "white", "#446CB3")),
         Manuae = cell_spec(Manuae, "html", color = ifelse(is.na(Manuae), "white", "#446CB3")),
         Tuamotu = cell_spec(Tuamotu, "html", color = ifelse(is.na(Tuamotu), "white", "#446CB3"))) %>% 
  kable(., 
        format = "html", escape = FALSE,
        caption = "Table 3. Number of individuals of each species by location. Only individuals with back-calculated data (by species) are shown.") %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  column_spec(1, italic = T)

Table 3. Number of individuals of each species by location. Only individuals with back-calculated data (by species) are shown.

Species	Gambiers	Moorea	Manuae	Marquesas	Tuamotu	Hao
Abudefduf sexfasciatus	11	2	NA	NA	NA	NA
Acanthurus achilles	6	NA	10	NA	NA	NA
Acanthurus lineatus	NA	NA	NA	8	NA	NA
Acanthurus nigricans	NA	NA	NA	8	NA	NA
Acanthurus pyroferus	NA	NA	NA	6	NA	NA
Acanthurus triostegus	3	11	NA	4	NA	NA
Balistapus undulatus	NA	12	NA	5	NA	NA
Caranx melampygus	NA	5	NA	NA	2	NA
Centropyge bispinosa	NA	7	NA	NA	NA	NA
Centropyge flavissima	9	5	NA	12	NA	NA
Cephalopholis argus	6	10	NA	NA	10	15
Cephalopholis urodeta	8	NA	NA	NA	NA	NA
Chaetodon citrinellus	NA	7	NA	NA	NA	NA
Chaetodon ornatissimus	4	NA	NA	6	NA	NA
Chlorurus spilurus	13	16	NA	NA	2	NA
Chromis iomelas	NA	15	NA	NA	NA	NA
Chromis viridis	6	NA	NA	NA	NA	NA
Ctenochaetus marginatus	NA	NA	NA	15	NA	NA
Ctenochaetus striatus	11	15	NA	NA	NA	NA
Dascyllus aruanus	NA	20	NA	NA	NA	NA
Dascyllus flavicaudus	8	NA	NA	NA	NA	NA
Epibulus insidiator	12	5	NA	NA	NA	NA
Epinephelus fasciatus	NA	NA	NA	10	NA	NA
Epinephelus hexagonatus	NA	NA	NA	NA	NA	14
Epinephelus merra	13	20	NA	NA	NA	13
Epinephelus polyphekadion	NA	NA	NA	NA	NA	14
Gnathodentex aureolineatus	8	NA	NA	NA	NA	NA
Gymnosarda unicolor	4	NA	NA	NA	NA	NA
Halichoeres trimaculatus	6	1	NA	NA	NA	NA
Lutjanus fulvus	NA	6	NA	NA	6	NA
Lutjanus kasmira	2	NA	NA	8	NA	NA
Mulloidichthys flavolineatus	NA	14	NA	NA	NA	NA
Myripristis berndti	7	15	NA	8	NA	NA
Naso lituratus	11	6	NA	NA	NA	NA
Odonus niger	NA	13	NA	4	NA	NA
Ostorhinchus angustatus	NA	15	NA	NA	NA	NA
Ostorhinchus apogonoides	NA	NA	NA	27	NA	NA
Parupeneus barberinus	5	NA	NA	1	NA	NA
Plectropomus laevis	12	NA	NA	NA	NA	18
Pristiapogon taeniopterus	NA	NA	NA	28	NA	NA
Sargocentron microstoma	NA	17	NA	5	NA	NA
Scarus psittacus	NA	2	NA	NA	5	NA
Siganus argenteus	6	2	NA	NA	3	NA
Stegastes nigricans	3	10	NA	NA	NA	NA
Zebrasoma scopas	5	13	NA	NA	NA	NA

1.1.4 Number of NA

# 1. Number of NA by variable

data_complete %>%
  summarise_all(~(sum(is.na(.)))) %>% 
  t(.) %>% 
  as.data.frame() %>% 
  kable(., col.names = c("NA"), caption = "Table 4. Number of rows with Non-Available (NA) data by variable") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Table 4. Number of rows with Non-Available (NA) data by variable

	NA
Family	0
Genus	0
Species	0
ID	0
Agei	0
Ri	387
Agecpt	0
Rcpt	0
Lcpt	0
L0p	0
R0p	2811
Li_sp_m	410
Li_sp_sd	410
Li_sploc_m	757
Li_sploc_sd	757
Weight	603
Location	0
Observer	0

1.2 Plot

1.2.1 Agei vs Li (sp.)

data_complete %>% 
  filter(!is.na(Li_sp_m)) %>% # Remove individuals with non back-calculated data
  ggplot(., aes(x = Agei, y = Li_sp_m)) +
  geom_point(color = col_color_graph, fill = col_fill_graph, size = 1, shape = 21)+
  theme(strip.text.x = element_text(size = 8, face = "italic"),
        strip.background = element_rect(colour = "black", fill = col_facet, size = 1),
        plot.title = element_text(colour = col_color_graph),
        plot.subtitle = element_text(colour = "black"),
        strip.text.y = element_text(angle = 360, face = "italic")) +
  facet_wrap(~Species, scales = "free", ncol = 5) +
  labs(x = "Age (years)", y = "Length (mm)")

Figure 1. Relation between length (TL, mm) and age, by species.

1.2.2 Agei vs Li (sp. and loc.)

data_complete %>% 
  filter(!is.na(Li_sploc_m)) %>% # Remove individuals with non back-calculated data
  ggplot(., aes(x = Agei, y = Li_sploc_m)) +
  geom_point(color = col_color_graph, fill = col_fill_graph, size = 1, shape = 21)+
  theme(strip.text.x = element_text(size = 8),
        strip.background = element_rect(colour = "black", fill = col_facet, size = 1),
        plot.title = element_text(colour = col_color_graph),
        plot.subtitle = element_text(colour = "black"),
        strip.text.y = element_text(angle = 360, face = "italic")) +
  facet_grid(Species~Location, scales = "free") +
  labs(x = "Age (years)", y = "Length (mm)")

Figure 2. Relation between length (TL, mm) and age, by species and location.

2. VBGF results

2.1 Growth parameters (sp.)

# 1. Growth parameters (on back-calculated data by species) ----

read.csv("./../data/03_back-calculated_vbgf_predictions_sp.csv") %>% 
  mutate(Estimate = paste0(round(Estimate, 3), " (", round(Est.Error, 3), ")")) %>% 
  pivot_wider("Species", names_from = Parameter, values_from = Estimate) %>% 
  select(Species, linf, k, t0) %>% 
  arrange(Species) %>% 
  kable(., 
        col.names = c("Species", "Linf", "K", "t0"), 
        caption = "Table 5. Values of Von Bertalanffy parameters estimated on back-calculated data through Bayesian framework by species. Standard deviation are the values in parentheses. Linf is expressed in TL and cm.") %>% 
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  column_spec(1, italic = T)

Table 5. Values of Von Bertalanffy parameters estimated on back-calculated data through Bayesian framework by species. Standard deviation are the values in parentheses. Linf is expressed in TL and cm.

Species	Linf	K	t0
Abudefduf sexfasciatus	14.846 (0.407)	1.102 (0.106)	-0.027 (0.025)
Acanthurus achilles	18.509 (0.806)	1.185 (0.153)	-0.023 (0.024)
Acanthurus lineatus	26.003 (1.769)	0.916 (0.182)	-0.044 (0.051)
Acanthurus nigricans	17.134 (0.968)	1.479 (0.212)	-0.011 (0.016)
Acanthurus pyroferus	19.594 (1.096)	0.786 (0.145)	-0.07 (0.067)
Acanthurus triostegus	15.754 (0.384)	0.738 (0.062)	-0.064 (0.032)
Balistapus undulatus	18.968 (1.875)	0.338 (0.057)	-0.28 (0.093)
Caranx melampygus	54.164 (4.165)	0.248 (0.04)	-0.193 (0.085)
Centropyge bispinosa	5.216 (0.824)	0.958 (0.938)	-0.066 (0.034)
Centropyge flavissima	9.82 (0.668)	0.422 (0.077)	-0.21 (0.078)
Cephalopholis argus	29.803 (1.204)	0.398 (0.021)	-0.252 (0.041)
Cephalopholis urodeta	20.912 (1.291)	0.142 (0.022)	-0.605 (0.101)
Chaetodon ornatissimus	14.523 (0.682)	1.006 (0.143)	-0.026 (0.036)
Chlorurus spilurus	22.758 (0.834)	1.049 (0.106)	-0.023 (0.022)
Chromis iomelas	5.002 (0.35)	1.297 (0.235)	-0.052 (0.021)
Chromis viridis	11.961 (1.086)	0.949 (0.236)	-0.049 (0.05)
Ctenochaetus marginatus	20.748 (1.626)	0.428 (0.047)	-0.133 (0.043)
Ctenochaetus striatus	17.1 (0.385)	0.851 (0.053)	-0.045 (0.022)
Dascyllus aruanus	5.588 (0.21)	0.966 (0.102)	-0.05 (0.025)
Dascyllus flavicaudus	8.948 (0.433)	0.504 (0.058)	-0.153 (0.057)
Epibulus insidiator	23.788 (1.344)	0.542 (0.067)	-0.123 (0.052)
Epinephelus fasciatus	20.4 (1.7)	0.718 (0.112)	-0.038 (0.029)
Epinephelus hexagonatus	21.366 (0.909)	0.634 (0.069)	-0.068 (0.042)
Epinephelus merra	17.617 (0.752)	0.859 (0.058)	-0.045 (0.018)
Epinephelus polyphekadion	38.026 (1.93)	0.299 (0.034)	-0.25 (0.093)
Gnathodentex aureolineatus	20.182 (0.846)	0.546 (0.074)	-0.156 (0.081)
Halichoeres trimaculatus	17.9 (1.669)	0.653 (0.146)	-0.034 (0.055)
Lutjanus fulvus	23.923 (0.93)	0.671 (0.083)	-0.087 (0.053)
Mulloidichthys flavolineatus	25.761 (0.56)	1.601 (0.112)	-0.008 (0.009)
Myripristis berndti	20.118 (0.718)	0.418 (0.032)	-0.236 (0.052)
Naso lituratus	29.808 (1.729)	1.846 (0.239)	-0.004 (0.009)
Odonus niger	25.093 (1.24)	0.459 (0.053)	-0.14 (0.05)
Ostorhinchus angustatus	7.195 (0.349)	0.69 (0.089)	-0.109 (0.036)
Ostorhinchus apogonoides	8.241 (0.217)	0.836 (0.052)	-0.073 (0.014)
Plectropomus laevis	71.842 (3.023)	0.318 (0.03)	-0.194 (0.056)
Pristiapogon taeniopterus	8.024 (0.283)	0.779 (0.069)	-0.094 (0.027)
Sargocentron microstoma	16.26 (0.56)	0.657 (0.054)	-0.056 (0.028)
Siganus argenteus	30.309 (3.329)	0.614 (0.135)	-0.056 (0.054)
Stegastes nigricans	12.04 (0.789)	0.403 (0.072)	-0.2 (0.114)

2.2 Growth parameters (sp. loc.)

# 1. Growth parameters (on back-calculated data by species and locations) ----

read.csv("./../data/03_back-calculated_vbgf_predictions_sploc.csv") %>% 
  mutate(Estimate = paste0(round(Estimate, 3), " (", round(Est.Error, 3), ")")) %>% 
  pivot_wider(c("Species", Location), names_from = Parameter, values_from = Estimate) %>% 
  select(Species, Location, linf, k, t0) %>% 
  arrange(Species) %>% 
  kable(., 
        col.names = c("Species", "Location", "Linf", "K", "t0"), 
        caption = "Table 6. Values of Von Bertalanffy parameters estimated on back-calculated data through Bayesian framework by species and location. Standard deviation are the values in parentheses. Linf is expressed in TL and cm.") %>% 
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  column_spec(1, italic = T)

Table 6. Values of Von Bertalanffy parameters estimated on back-calculated data through Bayesian framework by species and location. Standard deviation are the values in parentheses. Linf is expressed in TL and cm.

Species	Location	Linf	K	t0
Abudefduf sexfasciatus	Gambiers	14.837 (0.393)	1.103 (0.104)	-0.027 (0.024)
Acanthurus achilles	Manuae	19.44 (0.642)	1.203 (0.14)	-0.021 (0.025)
Acanthurus lineatus	Marquesas	24.726 (2.413)	1.044 (0.255)	-0.029 (0.039)
Acanthurus nigricans	Marquesas	17.06 (1.02)	1.495 (0.226)	-0.011 (0.015)
Acanthurus pyroferus	Marquesas	19.561 (1.02)	0.788 (0.142)	-0.07 (0.068)
Acanthurus triostegus	Moorea	15.673 (0.476)	0.742 (0.073)	-0.067 (0.038)
Balistapus undulatus	Marquesas	19.329 (2.174)	0.413 (0.151)	-0.254 (0.173)
Balistapus undulatus	Moorea	17.235 (1.905)	0.376 (0.066)	-0.237 (0.096)
Caranx melampygus	Moorea	54.835 (4.393)	0.242 (0.042)	-0.167 (0.102)
Centropyge bispinosa	Moorea	5.253 (0.849)	0.926 (0.565)	-0.066 (0.036)
Centropyge flavissima	Marquesas	6.773 (0.573)	1.076 (0.209)	-0.033 (0.021)
Centropyge flavissima	Moorea	8.175 (0.757)	1.262 (0.329)	-0.028 (0.026)
Centropyge flavissima	Gambiers	12.21 (1.244)	0.204 (0.041)	-0.834 (0.252)
Cephalopholis argus	Moorea	22.852 (2.469)	0.728 (0.149)	-0.068 (0.052)
Cephalopholis argus	Tuamotu	27.447 (1.451)	0.439 (0.064)	-0.225 (0.099)
Cephalopholis argus	Gambiers	40.416 (1.071)	0.297 (0.031)	-0.45 (0.14)
Cephalopholis argus	Hao	32.317 (1.775)	0.401 (0.035)	-0.184 (0.058)
Cephalopholis urodeta	Gambiers	20.981 (1.278)	0.141 (0.021)	-0.604 (0.102)
Chaetodon ornatissimus	Marquesas	14.599 (1.148)	1.077 (0.261)	-0.022 (0.043)
Chlorurus spilurus	Moorea	22.998 (1.313)	1.245 (0.214)	-0.016 (0.03)
Chlorurus spilurus	Gambiers	22.585 (1.059)	0.925 (0.12)	-0.037 (0.032)
Chromis iomelas	Moorea	4.994 (0.355)	1.301 (0.247)	-0.052 (0.021)
Chromis viridis	Gambiers	11.953 (1.072)	0.951 (0.228)	-0.048 (0.051)
Ctenochaetus marginatus	Marquesas	20.934 (1.533)	0.424 (0.043)	-0.135 (0.046)
Ctenochaetus striatus	Moorea	17.326 (0.566)	1.142 (0.098)	-0.021 (0.016)
Ctenochaetus striatus	Gambiers	16.99 (0.543)	0.566 (0.051)	-0.112 (0.049)
Dascyllus aruanus	Moorea	5.589 (0.201)	0.964 (0.101)	-0.05 (0.025)
Dascyllus flavicaudus	Gambiers	8.992 (0.403)	0.498 (0.054)	-0.152 (0.059)
Epibulus insidiator	Gambiers	23.859 (1.327)	0.539 (0.066)	-0.123 (0.052)
Epinephelus fasciatus	Marquesas	20.354 (1.664)	0.721 (0.117)	-0.038 (0.03)
Epinephelus hexagonatus	Hao	21.337 (0.907)	0.636 (0.068)	-0.067 (0.043)
Epinephelus merra	Moorea	14.078 (0.864)	1.192 (0.098)	-0.016 (0.014)
Epinephelus merra	Gambiers	18.52 (0.887)	0.655 (0.08)	-0.093 (0.052)
Epinephelus merra	Hao	22.481 (0.617)	0.842 (0.084)	-0.043 (0.034)
Epinephelus polyphekadion	Hao	38.812 (2.245)	0.248 (0.024)	-0.332 (0.083)
Gnathodentex aureolineatus	Gambiers	20.151 (0.881)	0.547 (0.076)	-0.155 (0.079)
Halichoeres trimaculatus	Gambiers	17.844 (1.584)	0.655 (0.157)	-0.036 (0.058)
Lutjanus fulvus	Moorea	21.82 (1.598)	1.175 (0.244)	-0.019 (0.025)
Lutjanus fulvus	Tuamotu	24.765 (1.386)	0.476 (0.076)	-0.215 (0.11)
Mulloidichthys flavolineatus	Moorea	25.762 (0.535)	1.601 (0.108)	-0.008 (0.009)
Myripristis berndti	Gambiers	23.324 (1.519)	0.338 (0.058)	-0.391 (0.157)
Myripristis berndti	Marquesas	21.795 (0.496)	0.378 (0.036)	-0.298 (0.101)
Myripristis berndti	Moorea	17.458 (0.9)	0.553 (0.064)	-0.11 (0.044)
Naso lituratus	Gambiers	31.83 (1.758)	1.718 (0.227)	-0.005 (0.011)
Odonus niger	Moorea	22.957 (1.147)	0.588 (0.071)	-0.08 (0.041)
Ostorhinchus angustatus	Moorea	7.216 (0.356)	0.686 (0.089)	-0.11 (0.036)
Ostorhinchus apogonoides	Marquesas	8.226 (0.217)	0.839 (0.053)	-0.073 (0.014)
Plectropomus laevis	Gambiers	77.388 (3.133)	0.253 (0.023)	-0.408 (0.097)
Plectropomus laevis	Hao	52.309 (2.535)	0.682 (0.054)	-0.012 (0.019)
Pristiapogon taeniopterus	Marquesas	8.025 (0.275)	0.778 (0.069)	-0.093 (0.027)
Sargocentron microstoma	Moorea	16.263 (0.542)	0.656 (0.052)	-0.056 (0.027)
Siganus argenteus	Gambiers	26.724 (2.244)	0.53 (0.098)	-0.091 (0.067)
Stegastes nigricans	Moorea	12.006 (0.886)	0.409 (0.099)	-0.199 (0.11)

2.3 Growth curves (sp.)

# 1. Growth curves ----

read.csv("./../data/03_back-calculated_vbgf_fitted_sp.csv") %>% 
  ggplot(data = .) +
    geom_ribbon(aes(x = age, ymin = ypred_lq, ymax = ypred_uq), alpha = 0.5, fill = col_fill_graph, alpha = 0.6) +
    geom_line(aes(x = age, y = ypred_m), color = col_color_graph) +
    geom_point(data = data_complete %>%
                 filter(Species %in% unique(read.csv("./../data/03_back-calculated_vbgf_fitted_sp.csv")$Species)) %>% 
                 mutate(Li_sp_m = Li_sp_m/10), 
               aes(x = Agei, y = Li_sp_m), color = col_color_graph, fill = col_fill_graph, size = 1, shape = 21) +
    facet_wrap(~Species, scales = "free", ncol = 5) +
    lims(y = c(0, NA)) +
    theme(strip.text.x = element_text(face = "italic")) +
    labs(x = "Age (years)", y = "Length (TL, cm)") +
    guides(colour = guide_legend(override.aes = list(alpha = 1, size = 0.5)))

Figure 3. Growth curves on back-calculated data by species.

2.4 Growth curves (sp. loc.)

# 1. Growth curves ----

read.csv("./../data/03_back-calculated_vbgf_fitted_sploc.csv") %>% 
  ggplot(data = .) +
    geom_ribbon(aes(x = age, ymin = ypred_lq, ymax = ypred_uq, fill = Location), alpha = 0.5, show.legend = FALSE) +
    geom_line(aes(x = age, y = ypred_m, color = Location), show.legend = FALSE) +
    geom_point(data = data_complete %>%
                 filter(Species %in% unique(read.csv("./../data/03_back-calculated_vbgf_fitted_sploc.csv")$Species)) %>% 
                 mutate(Li_sploc_m = Li_sploc_m/10), 
               aes(x = Agei, y = Li_sploc_m, color = Location), size = 1, show.legend = FALSE) +
    facet_grid(Species~Location, scales = "free") +
    lims(y = c(0, NA)) +
    labs(x = "Age (years)", y = "Length (TL, cm)") +
    theme(strip.text.x = element_text(size = 8),
          strip.background = element_rect(colour = "black", fill = col_facet, size = 1),
          plot.title = element_text(colour = col_color_graph),
          plot.subtitle = element_text(colour = "black"),
          strip.text.y = element_text(angle = 360, face = "italic"))

Figure 4. Growth curves on back-calculated data by species and location.

3. Raw vs Back-calculation

3.1 Growth curves (sp.)

# 1. List of species with enough individuals for one location ----

species_list <- c("Abudefduf sexfasciatus", "Acanthurus achilles", "Acanthurus pyroferus", 
                  "Acanthurus triostegus", "Balistapus undulatus", "Centropyge flavissima",
                  "Chaetodon ornatissimus", "Chromis iomelas", "Ctenochaetus marginatus",
                  "Dascyllus aruanus", "Epinephelus hexagonatus", "Myripristis berndti",
                  "Odonus niger", "Ostorhinchus angustatus", "Ostorhinchus apogonoides",
                  "Pristiapogon taeniopterus", "Sargocentron microstoma", "Stegastes nigricans")

# 1. Transform the raw data ----

# 1.1 Points --

data_raw_points <- read.csv("./../data/02_back-calculated-size-at-age_morat-et-al.csv") %>%
  filter(Species %in% species_list) %>% 
  select(Family, Genus, Species, ID, Agecpt, Lcpt, Location, Observer) %>% 
  unique() %>%
  dplyr::group_by(Species, Location) %>%
  dplyr::mutate(n = length(unique(ID))) %>%
  filter(n >= 10) %>% # filter with at least 10 replicates
  ungroup() %>% 
  dplyr::mutate(Lcpt = Lcpt/10) %>%  # Convert to cm
  mutate(Type = "Population-level data")

# 1.2 Curves -- 

data_raw_curve <- read.csv("./../data/03_raw_vbgf_fitted_sp.csv") %>% 
  filter(Species %in% species_list) %>% 
  mutate(Type = "Population-level data")

# 2. Transform the back-calculated data ----

# 2.1 Points --

data_backcalc_points <- read.csv("./../data/02_back-calculated-size-at-age_morat-et-al.csv") %>% 
  filter(Species %in% species_list) %>% 
  dplyr::mutate(Li_sp_m = Li_sp_m/10) %>%  # Convert to cm
  mutate(Type = "Back-calculated data")

# 2.2 Curves --

data_backcalc_curve <- read.csv("./../data/03_back-calculated_vbgf_fitted_sp.csv") %>% 
  filter(Species %in% species_list) %>% 
  mutate(Type = "Back-calculated data")

# 2. Make the plot ----

ggplot() +
  # Raw data
  geom_ribbon(data = data_raw_curve, aes(x = Agei, ymin = Q2.5, ymax = Q97.5, fill = Type), alpha = 0.5) +
  geom_line(data = data_raw_curve, aes(x = Agei, y = Estimate, color = Type), show.legend = FALSE) +
  geom_point(data = data_raw_points, aes(x = Agecpt, y = Lcpt, color = Type), size = 1) +
  # Back-calculated data
  geom_ribbon(data = data_backcalc_curve, aes(x = age, ymin = ypred_lq, ymax = ypred_uq, fill = Type), alpha = 0.5) +
  geom_line(data = data_backcalc_curve, aes(x = age, y = ypred_m, color = Type), show.legend = FALSE) +
  geom_point(data = data_backcalc_points, aes(x = Agei, y = Li_sp_m, color = Type), size = 1) +
  # Appearance
  facet_wrap(~Species, scales = "free", ncol = 3) +
  lims(y = c(0, NA)) +
  theme(strip.text.x = element_text(face = "italic"),
        legend.position = "top",
        legend.title = element_blank()) +
  labs(x = "Age (years)", y = "Length (TL, cm)") +
  lims(y = c(0, NA), x = c(0, NA)) +
  scale_color_manual(values = c(col_color_graph, "#d91e18")) +
  scale_fill_manual(values = c(col_fill_graph, "#e74c3c")) +
  guides(colour = guide_legend(override.aes = list(alpha = 1, size = 0.5)))

Figure 5. Comparison of growth curves by species between back-calculated data and population-level data.

# 3. Remove useless objects ----

rm(data_raw_points, data_raw_curve, data_backcalc_points, data_backcalc_curve)

3.2 Growth curves (sp. loc.)

# 1. Transform the raw data ----

species_list2 <- species_list[!species_list %in% c("Balistapus undulatus", "Centropyge flavissima", "Chaetodon ornatissimus")]

# 1.1 Points --

data_raw_points <- read.csv("./../data/02_back-calculated-size-at-age_morat-et-al.csv") %>%
  filter(Species %in% species_list2) %>% 
  select(Family, Genus, Species, ID, Agecpt, Lcpt, Location, Observer) %>% 
  unique() %>%
  dplyr::group_by(Species, Location) %>%
  dplyr::mutate(n = length(unique(ID))) %>%
  filter(n >= 10) %>% # filter with at least 10 replicates
  ungroup() %>% 
  dplyr::mutate(Lcpt = Lcpt/10) %>%  # Convert to cm
  mutate(Type = "Raw")

# 1.2 Curves -- 

data_raw_curve <- read.csv("./../data/03_raw_vbgf_fitted_sploc.csv") %>% 
  filter(Species %in% species_list2) %>% 
  mutate(Type = "Raw")

# 2. Transform the back-calculated data ----

# 2.1 Points --

data_backcalc_points <- read.csv("./../data/02_back-calculated-size-at-age_morat-et-al.csv") %>% 
  filter(Species %in% species_list2,
         !(Species == "Abudefduf sexfasciatus" & Location == "Moorea"),
         !(Species == "Acanthurus triostegus" & Location == "Marquesas"),
         !(Species == "Myripristis berndti" & Location == "Gambiers"),
         !(Species == "Myripristis berndti" & Location == "Marquesas")) %>% 
  dplyr::mutate(Li_sploc_m = Li_sploc_m/10) %>%  # Convert to cm
  mutate(Type = "Back-calculated")

# 2.2 Curves --

data_backcalc_curve <- read.csv("./../data/03_back-calculated_vbgf_fitted_sploc.csv") %>% 
  filter(Species %in% species_list2,
         !(Species == "Abudefduf sexfasciatus" & Location == "Moorea"),
         !(Species == "Acanthurus triostegus" & Location == "Marquesas"),
         !(Species == "Myripristis berndti" & Location == "Gambiers"),
         !(Species == "Myripristis berndti" & Location == "Marquesas")) %>% 
  mutate(Type = "Back-calculated")

# 2. Make the plot ----

ggplot() +
  # Raw data
  geom_ribbon(data = data_raw_curve, aes(x = Agei, ymin = Q2.5, ymax = Q97.5, fill = Type), alpha = 0.5) +
  geom_line(data = data_raw_curve, aes(x = Agei, y = Estimate, color = Type), show.legend = FALSE) +
  #geom_point(data = data_raw_points, aes(x = Agecpt, y = Lcpt, color = Type), size = 1) +
  # Back-calculated data
  geom_ribbon(data = data_backcalc_curve, aes(x = age, ymin = ypred_lq, ymax = ypred_uq, fill = Type), alpha = 0.5) +
  geom_line(data = data_backcalc_curve, aes(x = age, y = ypred_m, color = Type), show.legend = FALSE) +
  #geom_point(data = data_backcalc_points, aes(x = Agei, y = Li_sploc_m, color = Type), size = 1) +
  # Appearance
  facet_wrap(~Species, scales = "free", ncol = 3) +
  lims(y = c(0, NA)) +
  theme(strip.text.x = element_text(face = "italic"),
        legend.position = "top",
        legend.title = element_blank()) +  labs(x = "Age (years)", y = "Length (TL, cm)") +
  lims(y = c(0, NA), x = c(0, NA)) +
  scale_color_manual(values = c(col_color_graph, "#d91e18")) +
  scale_fill_manual(values = c(col_fill_graph, "#e74c3c")) +
  guides(colour = guide_legend(override.aes = list(alpha = 1, size = 0.5)))

Figure 6. Comparison of growth curves by species and location (one location only by growth curve) between back-calculated data and population-level data.

# 3. Save the plot ----

ggsave("./../figs/01_comparison-growth-curves-raw-vas-back-calculated.png", height = 15, width = 10) # PNG
ggsave("./../figs/01_comparison-growth-curves-raw-vas-back-calculated.eps", height = 15, width = 10) # PDF

# 4. Remove useless objects ----

rm(data_raw_points, data_raw_curve, data_backcalc_points, data_backcalc_curve)

4. Comparison with literature

# 1. Import file synthesizing the VBGF parameters from literature ----

vbgf_literature <- read_excel("./../data/00_von-bertalanffy-literature.xlsx", sheet = 1) %>% 
  dplyr::mutate(Linf = ifelse(Size_unit == "mm", Linf/10, Linf),
                Size_max = ifelse(Size_unit == "mm", Size_max/10, Size_max)) %>% # Convert all length values to cm
  select(-Family, -Genus) # Remove Family and Genus to add those level through fishbase

# 2. Check validity of species names ----

vbgf_literature <- vbgf_literature %>% 
  rowwise() %>% 
  dplyr::mutate(Species2 = ifelse(is_empty(validate_names(Species)) == TRUE, 
                           NA, 
                           validate_names(Species))) %>% # Correct the names through fishbase
  ungroup() %>% 
  dplyr::mutate(Species = str_replace_all(Species, c("Acanthurus chirugus" = "Acanthurus chirurgus",
                                                     "Scarus psitticus" = "Scarus psittacus"))) %>% # Correct unfound names
  filter(Species %in% unique(data_complete$Species)) %>% 
  mutate(Reference = paste0(Reference, " (", Reference_type, ")")) %>%
  mutate_at(c("Linf", "K", "t0"), as.character) %>% 
  select(-Size_unit, -Sex, -Reference_type, -Species2) %>% 
  arrange(Species)

4.1 Growth parameters

4.1.1 Table

# 1. Comparison of VBGF parameters between our study and literature ----

read.csv("./../data/03_back-calculated_vbgf_predictions_sp.csv") %>% 
  mutate(Estimate = paste0(round(Estimate, 3), " (", round(Est.Error, 3), ")")) %>% 
  pivot_wider(c("Species"), names_from = Parameter, values_from = Estimate) %>% 
  select(Species, linf, k, t0) %>% 
  rename(Linf = linf, K = k) %>% 
  mutate(Reference = "This study") %>% 
  full_join(., vbgf_literature) %>%
  select(Species, Linf, K, t0, Reference) %>% 
  arrange(Species) %>% 
  kable(.,
        format = "html", escape = FALSE,
        col.names = c("Species", "Linf", "K", "t0", "Reference"), 
        caption = "Table 6. Comparison between growth parameters estimated by species through Bayesian framework, with data from literature.") %>% 
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  column_spec(1, italic = T)

Table 6. Comparison between growth parameters estimated by species through Bayesian framework, with data from literature.

Species	Linf	K	t0	Reference
Abudefduf sexfasciatus	14.846 (0.407)	1.102 (0.106)	-0.027 (0.025)	This study
Acanthurus achilles	18.509 (0.806)	1.185 (0.153)	-0.023 (0.024)	This study
Acanthurus lineatus	26.003 (1.769)	0.916 (0.182)	-0.044 (0.051)	This study
Acanthurus lineatus	18.274	0.462	-0.32	Choat and Robertson, 2002 (Article)
Acanthurus lineatus	22.1	0.12	-15.6	Craig et al, 1997 (Article)
Acanthurus lineatus	18.909	0.68	NA	Gust et al, 2002 (Article)
Acanthurus lineatus	17.484	0.34	NA	Gust et al, 2002 (Article)
Acanthurus lineatus	17	0.416	-0.329	Ralston and Williams, 1988 (Report)
Acanthurus nigricans	17.134 (0.968)	1.479 (0.212)	-0.011 (0.016)	This study
Acanthurus nigricans	14.286	0.28	-0.69	Choat and Robertson, 2002 (Article)
Acanthurus pyroferus	19.594 (1.096)	0.786 (0.145)	-0.07 (0.067)	This study
Acanthurus triostegus	15.754 (0.384)	0.738 (0.062)	-0.064 (0.032)	This study
Balistapus undulatus	18.968 (1.875)	0.338 (0.057)	-0.28 (0.093)	This study
Caranx melampygus	54.164 (4.165)	0.248 (0.04)	-0.193 (0.085)	This study
Caranx melampygus	89.7	0.233	-0.044	Sudekum et al, 1991 (Article)
Centropyge bispinosa	5.216 (0.824)	0.958 (0.938)	-0.066 (0.034)	This study
Centropyge flavissima	9.82 (0.668)	0.422 (0.077)	-0.21 (0.078)	This study
Cephalopholis argus	29.803 (1.204)	0.398 (0.021)	-0.252 (0.041)	This study
Cephalopholis argus	50.6	0.075	-6.5	Donovan et al, 2013 (Article)
Cephalopholis argus	48.5499	0.049	-14.145	Mapleston et al, 2009 (Report)
Cephalopholis argus	44.22	0.26	1.33	Mehanna et al, 2019 (Article)
Cephalopholis argus	39.901	0.271	-0.169	Pears, 2005 (PhD Thesis)
Cephalopholis argus	37.119	0.339	-0.147	Pears, 2005 (PhD Thesis)
Cephalopholis argus	31.44	0.255	0	Moore et al, 2015b (Report)
Cephalopholis urodeta	20.912 (1.291)	0.142 (0.022)	-0.605 (0.101)	This study
Cephalopholis urodeta	20.645	0.31	NA	Payet et al, 2016 (Article)
Cephalopholis urodeta	14.5	1.39	NA	Fry et al, 2006 (Article)
Chaetodon citrinellus	9.07	2.15	-0.074	Berumen et al, 2005 (Article)
Chaetodon ornatissimus	14.523 (0.682)	1.006 (0.143)	-0.026 (0.036)	This study
Chlorurus spilurus	22.758 (0.834)	1.049 (0.106)	-0.023 (0.022)	This study
Chlorurus spilurus	34.4	0.4	-0.13	DeMartini et al, 2017 (Article)
Chlorurus spilurus	21.8	0.95	-0.075	Taylor and Choat, 2014 (Article)
Chromis iomelas	5.002 (0.35)	1.297 (0.235)	-0.052 (0.021)	This study
Chromis viridis	11.961 (1.086)	0.949 (0.236)	-0.049 (0.05)	This study
Ctenochaetus marginatus	20.748 (1.626)	0.428 (0.047)	-0.133 (0.043)	This study
Ctenochaetus striatus	17.1 (0.385)	0.851 (0.053)	-0.045 (0.022)	This study
Ctenochaetus striatus	17.2	0.75	-0.4	Trip et al, 2008 (Article)
Ctenochaetus striatus	18.2	0.5	-0.6	Trip et al, 2008 (Article)
Ctenochaetus striatus	17.6	1.4	-0.2	Trip et al, 2008 (Article)
Ctenochaetus striatus	17.8	0.7	-0.4	Trip et al, 2008 (Article)
Ctenochaetus striatus	15.9	0.5	-0.7	Trip et al, 2008 (Article)
Ctenochaetus striatus	12.8	0.6	-0.7	Trip et al, 2008 (Article)
Ctenochaetus striatus	19.4	1	-0.3	Trip et al, 2008 (Article)
Ctenochaetus striatus	17.1	0.9	-0.4	Trip et al, 2008 (Article)
Ctenochaetus striatus	20.1	0.8	-0.3	Trip et al, 2008 (Article)
Ctenochaetus striatus	19.9	0.3	-0.8	Trip et al, 2008 (Article)
Ctenochaetus striatus	23.2	1.3	-0.2	Trip et al, 2008 (Article)
Ctenochaetus striatus	20.5	2.1	-0.1	Trip et al, 2008 (Article)
Ctenochaetus striatus	18.5	1.1	-0.3	Trip et al, 2008 (Article)
Ctenochaetus striatus	18.8	0.4	-0.6	Trip et al, 2008 (Article)
Ctenochaetus striatus	19.4	0.4	-0.7	Trip et al, 2008 (Article)
Ctenochaetus striatus	16.8	0.591	-0.27	Choat and Robertson, 2002 (Article)
Ctenochaetus striatus	13.2	0.66	-0.16	Fidler et al, 2018 (Article)
Ctenochaetus striatus	11.9	0.72	-0.12	Fidler et al, 2018 (Article)
Ctenochaetus striatus	12.5	0.49	-0.36	Fidler et al, 2018 (Article)
Ctenochaetus striatus	11.9	0.72	-0.12	Fidler et al, 2018 (Article)
Ctenochaetus striatus	12.6	0.69	-0.05	Fidler et al, 2018 (Article)
Ctenochaetus striatus	11.9	0.72	-0.12	Fidler et al, 2018 (Article)
Ctenochaetus striatus	17	0.9	0.2	Ochavillo et al, 2011 (Article)
Ctenochaetus striatus	25.6	0.424	-0.643	Ralston and Williams, 1988 (Report)
Ctenochaetus striatus	16.92	0.63	0	Moore et al, 2013a (Report)
Ctenochaetus striatus	15.75	1.09	0	Moore et al, 2013b (Report)
Ctenochaetus striatus	16.8	0.98	0	Moore et al, 2015a (Report)
Ctenochaetus striatus	15.05	1.318	0	Moore et al, 2015b (Report)
Dascyllus aruanus	5.588 (0.21)	0.966 (0.102)	-0.05 (0.025)	This study
Dascyllus flavicaudus	8.948 (0.433)	0.504 (0.058)	-0.153 (0.057)	This study
Epibulus insidiator	23.788 (1.344)	0.542 (0.067)	-0.123 (0.052)	This study
Epibulus insidiator	19.894	0.25	-0.172	Hubble, 2003 (PhD Thesis)
Epinephelus fasciatus	20.4 (1.7)	0.718 (0.112)	-0.038 (0.029)	This study
Epinephelus fasciatus	28.1814	0.405	-0.685	Mapleston et al, 2009 (Report)
Epinephelus fasciatus	28.213	0.469	-0.198	Pears, 2005 (PhD Thesis)
Epinephelus fasciatus	27.46	0.45	-0.212	Pears, 2005 (PhD Thesis)
Epinephelus hexagonatus	21.366 (0.909)	0.634 (0.069)	-0.068 (0.042)	This study
Epinephelus merra	17.617 (0.752)	0.859 (0.058)	-0.045 (0.018)	This study
Epinephelus merra	21.2	0.5	-0.009	Pothin et al, 2004 (Article)
Epinephelus merra	16.46	1.01	0	Moore et al, 2013a (Report)
Epinephelus polyphekadion	38.026 (1.93)	0.299 (0.034)	-0.25 (0.093)	This study
Epinephelus polyphekadion	44.71	0.251	-0.14	Rhodes et al, 2011 (Article)
Epinephelus polyphekadion	57.9	0.18	0	Grandcourt, 2005 (Article)
Epinephelus polyphekadion	56.58	0.139	-1.18	Ohta et al, 2017 (Article)
Epinephelus polyphekadion	56.2345	0.194	-0.081	Mapleston et al, 2009 (Report)
Epinephelus polyphekadion	53.701	0.195	-0.245	Pears, 2005 (PhD Thesis)
Epinephelus polyphekadion	58.237	0.206	-0.212	Pears, 2005 (PhD Thesis)
Gnathodentex aureolineatus	20.182 (0.846)	0.546 (0.074)	-0.156 (0.081)	This study
Halichoeres trimaculatus	17.9 (1.669)	0.653 (0.146)	-0.034 (0.055)	This study
Lutjanus fulvus	23.923 (0.93)	0.671 (0.083)	-0.087 (0.053)	This study
Lutjanus fulvus	26.5	0.41	-0.49	Shimose and Nanami, 2014 (Article)
Lutjanus fulvus	20.34	1.306	0	Moore et al, 2015b (Report)
Lutjanus fulvus	23.62	0.735	0	Moore et al, 2015b (Report)
Lutjanus gibbus	35.13	0.29	-1	Moore, 2019 (Article)
Lutjanus gibbus	39.05	0.21	-1.88	Nanami et al, 2010 (Article)
Lutjanus gibbus	30.34	0.256	-3.05	Nanami et al, 2010 (Article)
Lutjanus gibbus	54.4	0.06	-9.48	Heupel et al, 2010b (Article)
Lutjanus gibbus	35.2	0.51	NA	Currey et al, 2010 (Report)
Lutjanus gibbus	27.4	0.78	-0.24	Holloway et al, 2015 (Article)
Lutjanus gibbus	34	0.38	0	Moore et al, 2013a (Report)
Lutjanus gibbus	29.35	0.44	0	Moore et al, 2013a (Report)
Lutjanus gibbus	28.47	0.53	0	Moore et al, 2013b (Report)
Lutjanus gibbus	26.5	0.57	0	Moore et al, 2013b (Report)
Lutjanus gibbus	30.5	0.57	0	Moore et al, 2015a (Report)
Lutjanus gibbus	26.7	0.83	0	Moore et al, 2015a (Report)
Lutjanus gibbus	32.48	0.547	0	Moore et al, 2015b (Report)
Lutjanus gibbus	26.67	0.729	0	Moore et al, 2015b (Report)
Lutjanus kasmira	34	0.29	-1.37	Morales-Nin and Ralston, 1990 (Article)
Lutjanus kasmira	29.6	0.384	-1.349	Ralston and Williams, 1988 (Report)
Lutjanus kasmira	22.37	0.6	0	Moore et al, 2013a (Report)
Lutjanus kasmira	19.75	0.91	0	Moore et al, 2013a (Report)
Mulloidichthys flavolineatus	25.761 (0.56)	1.601 (0.112)	-0.008 (0.009)	This study
Mulloidichthys flavolineatus	38	0.27	-1.15	Mehanna et al, 2017 (Article)
Myripristis berndti	20.118 (0.718)	0.418 (0.032)	-0.236 (0.052)	This study
Naso lituratus	29.808 (1.729)	1.846 (0.239)	-0.004 (0.009)	This study
Naso lituratus	21.2	1.38	-0.2	Taylor et al, 2014 (Article)
Naso lituratus	20.4	0.93	-0.3	Taylor et al, 2014 (Article)
Naso lituratus	27.05	0.92	0	Moore et al, 2013a (Report)
Naso lituratus	23.22	1.05	0	Moore et al, 2013a (Report)
Naso lituratus	26.59	0.79	0	Moore et al, 2013b (Report)
Naso lituratus	23.43	1.11	0	Moore et al, 2013b (Report)
Naso lituratus	23.2	1.1	0	Moore et al, 2015a (Report)
Naso lituratus	19.9	1.92	0	Moore et al, 2015a (Report)
Naso lituratus	23.77	1.171	0	Moore et al, 2015b (Report)
Naso lituratus	20.72	2.055	0	Moore et al, 2015b (Report)
Naso unicornis	47.8	0.44	-0.12	Andrews et al, 2016 (Article)
Naso unicornis	47.1	0.36	-0.14	Taylor et al, 2014 (Article)
Naso unicornis	49.3	0.22	-0.48	Taylor et al, 2014 (Article)
Naso unicornis	38.577	0.489	-0.14	Choat and Robertson, 2002 (Article)
Odonus niger	25.093 (1.24)	0.459 (0.053)	-0.14 (0.05)	This study
Ostorhinchus angustatus	7.195 (0.349)	0.69 (0.089)	-0.109 (0.036)	This study
Ostorhinchus apogonoides	8.241 (0.217)	0.836 (0.052)	-0.073 (0.014)	This study
Plectropomus laevis	71.842 (3.023)	0.318 (0.03)	-0.194 (0.056)	This study
Plectropomus laevis	101.5	0.19	0	Grandcourt, 2005 (Article)
Plectropomus laevis	115.9	0.096	-2.28	Heupel et al, 2010a (Article)
Pristiapogon taeniopterus	8.024 (0.283)	0.779 (0.069)	-0.094 (0.027)	This study
Sargocentron microstoma	16.26 (0.56)	0.657 (0.054)	-0.056 (0.028)	This study
Scarus psittacus	17.21	1.19	-0.05	Choat and Robertson, 2002 (Article)
Scarus psittacus	32.7	0.486	-0.01	DeMartini et al, 2017 (Article)
Scarus psittacus	21.7	1.653	-0.29	Page, 1998 (Report)
Scarus psittacus	20.7	0.91	-0.083	Taylor and Choat, 2014 (Article)
Siganus argenteus	30.309 (3.329)	0.614 (0.135)	-0.056 (0.054)	This study
Siganus argenteus	27.4	0.9	-0.3	Taylor et al, 2016 (Article)
Stegastes nigricans	12.04 (0.789)	0.403 (0.072)	-0.2 (0.114)	This study
Zebrasoma scopas	13.282	0.425	-0.49	Choat and Robertson, 2002 (Article)

4.1.2 Plot

# 1. Comparison of VBGF parameters between our study and literature ----

vbgf_literature %>% 
  select(Species, Linf, K, t0, Reference) %>% 
  pivot_longer(c("Linf", "K", "t0"), names_to = "Parameter", values_to = "Estimate") %>% 
  mutate(Estimate = as.numeric(Estimate),
         Type = "Literature") %>% 
  bind_rows(., read.csv("./../data/03_back-calculated_vbgf_predictions_sp.csv") %>% 
                select(-Est.Error, -Q2.5, -Q97.5) %>% 
                mutate(Parameter = str_replace_all(Parameter, c("linf" = "Linf",
                                                                "k" = "K")),
                       Type = "This study",
                       Reference = "This study")) %>% 
  mutate(Species = as.factor(Species),
         Parameter = factor(Parameter, levels = c("Linf", "K", "t0"))) %>% 
  ggplot(data = ., aes(x = reorder(Species, desc(Species)), y = Estimate, color = Type)) +
    geom_point() +
    facet_wrap(~Parameter, scales = "free") +
    coord_flip() +
    labs(x = NULL, y = "Value") +
    theme(axis.text.y = element_text(face = "italic"))

Figure 8. Comparison of VBGF growth parameters estimated through Bayesian framework on species back-calculated data, with parameters from literature.

4.2 Growth curves

# 1. Get predictions from literature data ----

vbgf_literature_pred <- vbgf_literature %>% 
  # Complete missing Age_max
  filter(is.na(Age_max)) %>% 
  left_join(., vbgf_literature %>% 
              group_by(Species) %>% 
              summarise(Age_max2 = max(Age_max, na.rm = TRUE)) %>% 
              mutate(Age_max2 = na_if(Age_max2, -Inf))) %>% 
  mutate(Age_max = ifelse(is.na(Age_max), Age_max2, Age_max)) %>% 
  select(-Age_max2) %>% 
  bind_rows(., vbgf_literature %>% 
              filter(!(is.na(Age_max)))) %>% 
  arrange(Species) %>% 
  # Fix missing t0 to 0
  dplyr::mutate(t0 = ifelse(is.na(t0), 0, t0)) %>% 
  filter(!(is.na(K)), !(is.na(Linf)), !(is.na(t0))) %>% 
  mutate_at(c("Linf", "K", "t0"), as.numeric) %>% 
  filter(Species %in% unique(read.csv("./../data/03_back-calculated_vbgf_fitted_sp.csv")$Species)) %>% 
  # Make the predictions
  dplyr::mutate(Line = row_number()) %>% 
  group_by(Line) %>% 
  do(pred_vbgf(data = .))

# 2. Make the plot ----

plot_final <- read.csv("./../data/03_back-calculated_vbgf_fitted_sp.csv") %>% 
  filter(Species %in% unique(vbgf_literature_pred$Species)) %>% 
  ggplot(data = .) +
  geom_line(data = vbgf_literature_pred, aes(x = Agei, y = Li, group = as.factor(Line), 
                                             text = Reference), color = "#6c7a89", size = 0.7) +
  geom_ribbon(aes(x = age, ymin = ypred_lq, ymax = ypred_uq), alpha = 0.5, fill = col_fill_graph) +
  geom_line(aes(x = age, y = ypred_m), size = 0.7, color = col_color_graph) +

  facet_wrap(~Species, ncol = 4, scales = "free") +
  labs(x = "Age (years)", y = "Length (cm)") +
  theme(strip.text.x = element_text(face = "italic")) +
  lims(y = c(0, NA), x = c(0, NA)) +
  guides(colour = guide_legend(override.aes = list(alpha = 1, size = 0.5)))

plot_final

Figure 9. Comparison of growth curves based on VBGF growth parameters estimated through Bayesian framework on species back-calculated data with growth curves from literature.

# 4. Convert to plotly ----

plot_final <- plot_final +
  theme(legend.position = "none")

ggplotly(plot_final, tooltip = c("Reference", "Location", "Size_type"))

Figure 10. Comparison of growth curves based on VBGF growth parameters estimated through Bayesian framework on species back-calculated data with growth curves from literature (interactive plot).

4.3 Maximum age

# 1. Comparison of the maximum age between our study and literature ----

vbgf_literature %>% 
  filter(!(is.na(Age_max))) %>% 
  group_by(Species) %>% 
  summarise(Age_max = max(Age_max)) %>% 
  ungroup() %>% 
  left_join(., vbgf_literature) %>% 
  select(Species, Age_max, Reference) %>% 
  right_join(., data_complete %>% 
                  mutate(Lcpt = Lcpt/10) %>% 
                  filter(!(is.na(Agecpt))) %>% 
                  group_by(Species) %>% 
                  summarise(Agecpt = max(Agecpt)) %>% 
                  ungroup()) %>% 
  select(Species, Agecpt, Age_max, Reference) %>% 
  arrange(Species) %>%
  mutate(Agecpt = as.numeric(as.character(Agecpt)),
         Age_max = as.numeric(as.character(Age_max))) %>% 
  mutate(Species = cell_spec(Species, "html", color = "black"),
         Agecpt = cell_spec(Agecpt, "html", color = "black"),
         Age_max = cell_spec(Age_max, "html", color = ifelse(is.na(Age_max), "white", "black")),
         Reference = cell_spec(Reference, "html", color = ifelse(is.na(Reference), "white", "black"))) %>% 
  kable(., 
        format = "html", escape = FALSE,
        col.names = c("Species", "Age max.", "Age max.", "Reference"),
        caption = "Table 7. Comparison of maximum age (Age max.) between this study and litterature. When value of Age max. from literature is missing for one species, it can be either due to unspecified maximum age in the reference or absent value in literature for the considered species.") %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>% 
  column_spec(1, italic = T) %>%
  add_header_above(c(" " = 1, "This Study" = 1, "Litterature" = 2))

Table 7. Comparison of maximum age (Age max.) between this study and litterature. When value of Age max. from literature is missing for one species, it can be either due to unspecified maximum age in the reference or absent value in literature for the considered species.

	This Study	Litterature
Species	Age max.	Age max.	Reference
Abudefduf sexfasciatus	13	NA	NA
Acanthurus achilles	27	NA	NA
Acanthurus lineatus	23	42	Gust et al, 2002 (Article)
Acanthurus nigricans	9	NA	NA
Acanthurus pyroferus	19	NA	NA
Acanthurus triostegus	12	NA	NA
Balistapus undulatus	18	NA	NA
Caranx melampygus	15	6	Sudekum et al, 1991 (Article)
Centropyge bispinosa	11	NA	NA
Centropyge flavissima	27	NA	NA
Cephalopholis argus	21	39	Mapleston et al, 2009 (Report)
Cephalopholis urodeta	17	12	Payet et al, 2016 (Article)
Chaetodon citrinellus	6	7	Berumen et al, 2005 (Article)
Chaetodon ornatissimus	10	NA	NA
Cheilinus chlorourus	6	NA	NA
Chlorurus spilurus	16	11	DeMartini et al, 2017 (Article)
Chromis iomelas	5	NA	NA
Chromis viridis	9	NA	NA
Ctenochaetus marginatus	13	NA	NA
Ctenochaetus striatus	17	37	Trip et al, 2008 (Article)
Dascyllus aruanus	7	NA	NA
Dascyllus flavicaudus	13	NA	NA
Epibulus insidiator	16	16	Hubble, 2003 (PhD Thesis)
Epinephelus fasciatus	13	21	Mapleston et al, 2009 (Report)
Epinephelus hexagonatus	14	NA	NA
Epinephelus merra	17	6	Pothin et al, 2004 (Article)
Epinephelus merra	17	6	Moore et al, 2013a (Report)
Epinephelus polyphekadion	20	44	Mapleston et al, 2009 (Report)
Gnathodentex aureolineatus	17	NA	NA
Gymnosarda unicolor	7	NA	NA
Halichoeres trimaculatus	5	NA	NA
Lutjanus fulvus	21	34	Shimose and Nanami, 2014 (Article)
Lutjanus gibbus	1	38	Moore, 2019 (Article)
Lutjanus kasmira	30	6	Moore et al, 2013a (Report)
Lutjanus kasmira	30	6	Moore et al, 2013a (Report)
Monotaxis grandoculis	20	NA	NA
Mulloidichthys flavolineatus	6	6	Mehanna et al, 2017 (Article)
Myripristis berndti	24	NA	NA
Naso lituratus	10	20	Moore et al, 2013a (Report)
Naso lituratus	10	20	Moore et al, 2013a (Report)
Naso lituratus	10	20	Moore et al, 2013b (Report)
Naso lituratus	10	20	Moore et al, 2013b (Report)
Naso unicornis	17	50	Andrews et al, 2016 (Article)
Odonus niger	16	NA	NA
Ostorhinchus angustatus	6	NA	NA
Ostorhinchus apogonoides	7	NA	NA
Parupeneus barberinus	6	NA	NA
Plectropomus laevis	22	16	Heupel et al, 2010a (Article)
Pristiapogon taeniopterus	8	NA	NA
Sargocentron microstoma	13	NA	NA
Scarus psittacus	6	6	DeMartini et al, 2017 (Article)
Scarus psittacus	6	6	Taylor and Choat, 2014 (Article)
Siganus argenteus	13	NA	NA
Siganus spinus	3	NA	NA
Stegastes albifasciatus	8	NA	NA
Stegastes nigricans	13	NA	NA
Zebrasoma scopas	24	NA	NA

5. Annex

# 1. Merge the tables on growth parameters by species and by species and location in one table ----

table_final <- read.csv("./../data/03_back-calculated_vbgf_predictions_sp.csv") %>% 
  mutate(Location = "All") %>% 
  bind_rows(., read.csv("./../data/03_back-calculated_vbgf_predictions_sploc.csv")) %>% 
  select(-Est.Error) %>% 
  pivot_wider(names_from = Parameter, names_glue = "{Parameter}_{.value}", values_from = c(Estimate, Q2.5, Q97.5)) %>% 
  arrange(Species, Location) %>% 
  select(Species, Location, linf_Estimate, linf_Q2.5, linf_Q97.5, k_Estimate, k_Q2.5, k_Q97.5,
         t0_Estimate, t0_Q2.5, t0_Q97.5) %>% 
  group_by(Species) %>% 
  mutate(n = n()) %>% 
  filter(n > 2 | Location == "All")

# Ajouter le n indi. (et le n n points ?) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

Reproducibility

# 1. Reproducibility ----

sessionInfo()

## R version 4.0.1 (2020-06-06)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=French_France.1252  LC_CTYPE=French_France.1252   
## [3] LC_MONETARY=French_France.1252 LC_NUMERIC=C                  
## [5] LC_TIME=French_France.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] rfishbase_3.0.4     plotly_4.9.2.1      readxl_1.3.1       
##  [4] formattable_0.2.0.1 kableExtra_1.1.0    forcats_0.5.0      
##  [7] stringr_1.4.0       dplyr_1.0.0         purrr_0.3.4        
## [10] readr_1.3.1         tidyr_1.1.0         tibble_3.0.1       
## [13] ggplot2_3.3.2       tidyverse_1.3.0     extrafont_0.17     
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.4.6      lubridate_1.7.9   lattice_0.20-41   assertthat_0.2.1 
##  [5] digest_0.6.25     R6_2.4.1          cellranger_1.1.0  backports_1.1.7  
##  [9] reprex_0.3.0      evaluate_0.14     highr_0.8         httr_1.4.1       
## [13] pillar_1.4.4      rlang_0.4.6       curl_4.3          lazyeval_0.2.2   
## [17] data.table_1.12.8 rstudioapi_0.11   extrafontdb_1.0   blob_1.2.1       
## [21] rmarkdown_2.3     labeling_0.3      webshot_0.5.2     htmlwidgets_1.5.1
## [25] munsell_0.5.0     broom_0.5.6       compiler_4.0.1    modelr_0.1.8     
## [29] xfun_0.14         pkgconfig_2.0.3   htmltools_0.5.0   tidyselect_1.1.0 
## [33] fansi_0.4.1       viridisLite_0.3.0 crayon_1.3.4      dbplyr_1.4.4     
## [37] withr_2.2.0       grid_4.0.1        nlme_3.1-148      jsonlite_1.6.1   
## [41] Rttf2pt1_1.3.8    gtable_0.3.0      lifecycle_0.2.0   DBI_1.1.0        
## [45] magrittr_1.5      scales_1.1.1      cli_2.0.2         stringi_1.4.6    
## [49] farver_2.0.3      fs_1.4.1          xml2_1.3.2        ellipsis_0.3.1   
## [53] generics_0.0.2    vctrs_0.3.1       gh_1.1.0          tools_4.0.1      
## [57] glue_1.4.1        crosstalk_1.1.0.1 hms_0.5.3         yaml_2.2.1       
## [61] colorspace_1.4-1  rvest_0.3.6       memoise_1.1.0     knitr_1.28       
## [65] haven_2.3.1

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Jeremy WICQUART | jeremywicquart@gmail.com | 2020-08-17 07:23:46